The rapid adoption of deep learning in sensitive domains has brought tremendous benefits. However, this widespread adoption has also given rise to serious vulnerabilities, particularly model inversion (MI) attacks, posing a significant threat to the privacy and integrity of personal data. The increasing prevalence of these attacks in applications such as biometrics, healthcare, and finance has created an urgent need to understand their mechanisms, impacts, and defense methods. This survey aims to fill the gap in the literature by providing a structured and in-depth review of MI attacks and defense strategies. Our contributions include a systematic taxonomy of MI attacks, extensive research on attack techniques and defense mechanisms, and a discussion about the challenges and future research directions in this evolving field. By exploring the technical and ethical implications of MI attacks, this survey aims to offer insights into the impact of AI-powered systems on privacy, security, and trust. In conjunction with this survey, we have developed a comprehensive repository to support research on MI attacks and defenses. The repository includes state-of-the-art research papers, datasets, evaluation metrics, and other resources to meet the needs of both novice and experienced researchers interested in MI attacks and defenses, as well as the broader field of AI security and privacy. The repository will be continuously maintained to ensure its relevance and utility. It is accessible at https://github.com/overgter/Deep-Learning-Model-Inversion-Attacks-and-Defenses.

Accurate household electricity load forecasting is becoming increasingly vital with the continued growth of smart grids, household renewable energy systems, and smart meter deployment. Unlike regional or grid load forecasting, household-level forecasting presents unique challenges due to highly irregular consumption patterns, data scarcity, privacy concerns, and behavioral variability. In recent years, artificial intelligence (AI) methods have demonstrated strong potential to address these complexities, enabling more accurate, robust, and adaptive forecasting systems. This survey presents a comprehensive and up-to-date review, with a focus on AI-based techniques specifically tailored to household-level forecasting. A key contribution of this work is the development of a challenge-centered taxonomy that categorizes methods based on four critical problem domains: methodological limitations, data-related constraints, behavioral complexity, and privacy and security concerns. By aligning representative AI approaches with these core challenges, the survey offers a structured and insightful understanding of the current research landscape. It also provides a comparative analysis with prior surveys, identifies gaps in the literature, and highlights promising research directions, including multimodal learning, adaptive modeling, integration of large language models, and privacy-preserving forecasting. This work could serve as a valuable resource for researchers and practitioners aiming to advance intelligent and trustworthy forecasting solutions in household energy systems.

Accurate household electricity load forecasting is becoming increasingly vital with the continued growth of smart grids, household renewable energy systems, and smart meter deployment. Unlike regional or grid load forecasting, household-level forecasting presents unique challenges due to highly irregular consumption patterns, data scarcity, privacy concerns, and behavioral variability. In recent years, artificial intelligence (AI) methods have demonstrated strong potential to address these complexities, enabling more accurate, robust, and adaptive forecasting systems. This survey presents a comprehensive and up-to-date review, with a focus on AI-based techniques specifically tailored to household-level forecasting. A key contribution of this work is the development of a challengecentered taxonomy that categorizes methods based on four critical problem domains: methodological limitations, data-related constraints, behavioral complexity, and privacy and security concerns. By aligning representative AI approaches with these core challenges, the survey offers a structured and insightful understanding of the current research landscape. It also provides a comparative analysis with prior surveys, identifies gaps in the literature, and highlights promising research directions, including multimodal learning, adaptive modeling, integration of large language models, and privacypreserving forecasting. This work could serve as a valuable resource for researchers and practitioners aiming to advance intelligent and trustworthy forecasting solutions in household energy systems.

With the rapid advancement of artificial intelligence and deep learning, medical image analysis has become a critical tool in modern healthcare, significantly improving diagnostic accuracy and efficiency. However, AI-based methods also raise serious privacy concerns, as medical images often contain highly sensitive patient information. This review offers a comprehensive overview of privacy-preserving techniques in medical image analysis, including encryption, differential privacy, homomorphic encryption, federated learning, and generative adversarial networks. We explore the application of these techniques across various medical image analysis tasks, such as diagnosis, pathology, and telemedicine. Notably, we organizes the review based on specific challenges and their corresponding solutions in different medical image analysis applications, so that technical applications are directly aligned with practical issues, addressing gaps in the current research landscape. Additionally, we discuss emerging trends, such as zero-knowledge proofs and secure multi-party computation, offering insights for future research. This review serves as a valuable resource for researchers and practitioners and can help advance privacy-preserving in medical image analysis.